Project details for OptWok

Screenshot OptWok 0.3.1

by ong - May 2, 2013, 10:46:11 CET [ Project Homepage BibTeX Download ]

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Description:

A collection of python code to perform research in optimization. The aim is to provide reusable components that can be quickly applied to machine learning problems.

Dependencies:

  • python 2.5+
  • cvxopt 1.0+ (for solving linear and quadratic programs)
  • pythongrid (for using a cluster)
  • cython 0.14.1 (for speeding up kernel computations)

Description

The projects currently prototyped:

  • Ellipsoidal multiple instance learning
  • Contextual bandits upper confidence bound algorithm (using GP)
  • learning the output kernel using block coordinate descent
  • difference of convex functions algorithms for sparse classfication

Ellipsoidal Multiple Instance Learning

  • The code for eMIL is contained in mil.mi_classifier_emil
  • emil_demo.py gives usage examples
  • mil.mi_data provides containers for multiple instance learning data

Learning the output kernel using block coordinate descent

  • The workhorse is kernelopt.py, which implements a framework for minimizing the invex function for learning the kernel on outputs.
  • multiclass_demo.py gives usage examples.
Changes to previous version:
  • minor bugfix
BibTeX Entry: Download
URL: Project Homepage
Supported Operating Systems: Agnostic
Data Formats: Matlab, Hdf, Numpy, Json
Tags: Kernel Learning
Archive: download here

Other available revisons

Version Changelog Date
0.3.1
  • minor bugfix
May 2, 2013, 10:46:11
0.3
  • Included code for Gaussian Process Contextual Bandits
  • Implemented Ellipsoidal Multiple Instance Learning
  • difference of convex functions algorithms for sparse classfication
April 4, 2013, 12:48:56
0.2.1
  • Fixed missing multiclass module.
  • Slycot sources no longer distributed, using github project instead.
July 21, 2011, 20:39:12
0.2

Initial Announcement on mloss.org.

May 1, 2011, 15:30:29

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